Detecting a Midlatitude Island Climate Signature in the Great Lakes Coastal Region of Ontario, Canada
Bibliographic record
Abstract
A day-to-day temperature (DTD) variability metric was used to detect marine coastal climates in the province of Ontario, Canada. Eleven of fourteen climate stations on islands, most in the Great Lakes and two in other large water bodies, displayed marine characteristics using a day-to-day temperature metric threshold developed for ocean coastal locations in China and Canada with values below 2.35 for the daily minimum temperature variability. Detailed comparisons with neighbouring coastal stations were conducted for six focal areas in the Great Lakes and the marine effect on the local climate was unambiguously demonstrated in a statistically significant manner. Those displaying marine characteristics were all island climate stations, usually at an elevation close to the lake level, and the marine influence, as measured by day-to-day temperature variability, dropped off rapidly with distance and elevation from the local water body. The DTD metric was compared to a diurnal temperature range (DTR) metric. While DTR was able to distinguish in a statistically significant manner between islands and inland stations, an unambiguous threshold between the two was not possible, unlike DTD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".